Systems and methods for ultrasound imaging

ABSTRACT

In some embodiments, a method comprises: obtaining a 2D ultrasound image of an imaged region of a subject, the imaged region comprising bone; identifying model template cross-sections of a 3D model of the bone corresponding to the 2D image at least in part by registering the 2D ultrasound image to the 3D model, wherein the model template cross-sections are defined prior to obtaining such 2D image, the model template cross-sections having size and shape representative of a population of potential subjects; identifying at least one location of at least one landmark feature of the bone in the 2D image based on results of the registration; and generating a visualization that includes: a visualization of the 2D image and a visualization of one of the identified cross-sections of the 3D model, wherein the visualization indicates the at least one location of the at least one landmark feature.

RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/008,743, filed on Jun. 14, 2018 entitled“SYSTEMS AND METHODS FOR ULTRASOUND IMAGING,” which claims priority to,and is related to, U.S. patent application Ser. No. 14/770,893, filed onAug. 27, 2015, entitled “SYSTEMS AND METHODS FOR ULTRASOUND IMAGING,”which claims priority to, is related to, and is a U.S. nationalapplication based on International Application No. PCT/US2014/018732,filed on Feb. 26, 2014, now International Publication No. WO 2014/134188A1, entitled “SYSTEMS AND METHODS FOR ULTRASOUND IMAGING,” which claimsthe benefit of and priority to U.S. Provisional Application No.61/770,437, filed on Feb. 28, 2013, entitled “MODEL REGISTRATION-BASEDIMAGING TECHNIQUES AND ASSOCIATED SYSTEMS AND DEVICES,” each of whichare incorporated by reference herein in its entirety.

STATEMENT OF FEDERALLY SPONSORED RESEARCH

This invention was made with government support under award numberR43EB015232 awarded by the National Institute of Biomedical Imaging andBioengineering of the National Institutes of Health and award number1214788 awarded by the National Science Foundation. The government hascertain rights in the invention.

TECHNICAL FIELD

Aspects of the technology described herein relate to ultrasound imagingand related systems and methods. Some aspects relate to generatingultrasound images of bone in a subject being imaged. Some aspects relateto visualizing ultrasound images of bone in a subject being imaged.

BACKGROUND

Medical ultrasound may be used as an alternative to X-ray for boneimaging. However, conventional ultrasound systems are limited in theirapplication. For example, in many conventional ultrasound systems,artifacts may be generated from off-axis reflections, which make theproduced image less useful to the user. In addition, many conventionalsystems produce difficult-to-interpret two-dimensional (2D) images.Although certain transducer geometries may be used to reduce artifactsand three-dimensional (3D) ultrasound images of bone may be obtained,such images nonetheless generally suffer from low sensitivity, as theultrasound signal strength is highly dependent on the angle of the bonesurface with respect to the acoustic beam axis. Therefore, while theerror of reconstructed bone surfaces may be very low, the lowspecificity and sensitivity of the reconstruction may still yield animage that is challenging to interpret. Additionally, the production offreehand images in 3D remains challenging due to, for example,cumulative motion estimation bias distortions. For at least thesereasons, ultrasound images generated by conventional ultrasound imagingtechniques may be difficult to interpret.

SUMMARY

Some embodiments are directed to a method of processing ultrasound data.The method comprises using at least one computer hardware processor toperform obtaining ultrasound data generated based, at least in part, onone or more ultrasound signals from an imaged region of a subject;calculating shadow intensity data corresponding to the ultrasound data;generating, based at least in part on the shadow intensity data and atleast one bone separation parameter, an indication of bone presence inthe imaged region; generating, based at least in part on the shadowintensity data and at least one tissue separation parameter differentfrom the at least one bone separation parameter, an indication of tissuepresence in the imaged region; and generating an ultrasound image of thesubject at least in part by combining the indication of bone presenceand the indication of tissue presence.

Some embodiments are directed to a system for processing ultrasounddata. The system comprises at least one computer hardware processorconfigured to perform obtaining ultrasound data generated based, atleast in part, on one or more ultrasound signals from an imaged regionof a subject; calculating shadow intensity data corresponding to theultrasound data; generating, based at least in part on the shadowintensity data and at least one bone separation parameter, an indicationof bone presence in the imaged region, generating, based at least inpart on the shadow intensity data and at least one tissue separationparameter different from the at least one bone separation parameter, anindication of tissue presence in the imaged region; and generating anultrasound image of the subject at least in part by combining theindication of bone presence and the indication of tissue presence.

Some embodiments are directed to at least one non-transitorycomputer-readable storage medium storing processor executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor to performa method of processing ultrasound data. The method comprises obtainingultrasound data generated based, at least in part, on one or moreultrasound signals from an imaged region of a subject; calculatingshadow intensity data corresponding to the ultrasound data; generating,based at least in part on the shadow intensity data and at least onebone separation parameter, an indication of bone presence in the imagedregion; generating, based at least in part on the shadow intensity dataand at least one tissue separation parameter different from the at leastone bone separation parameter, an indication of tissue presence in theimaged region; and generating an ultrasound image of the subject atleast in part by combining the indication of bone presence and theindication of tissue presence.

In some embodiments, the ultrasound data comprises a plurality ofultrasound data values each corresponding to a respective voxel in aplurality of voxels, and wherein calculating the shadow intensity datacomprises calculating a shadow intensity value for a first of theplurality of voxels at least in part by calculating a weighted sum ofultrasound data values corresponding to voxels in the plurality ofvoxels that are located at least a threshold number of voxels away fromthe first voxel.

In some embodiments, including any of the preceding embodiments, thethreshold number of voxels is greater than or equal to an axialresolution of an imaging system used to generate the ultrasound data.

In some embodiments, including any of the preceding embodiments, theultrasound data comprises a plurality of ultrasound data values eachcorresponding to a respective voxel in a plurality of voxels, andwherein generating the indication of bone presence in the imaged regioncomprises calculating, for a first of the plurality of voxels, a boneintensity value based at least in part on a ratio of a first ultrasounddata value corresponding to the first voxel and a first shadow intensityvalue corresponding to the first voxel.

In some embodiments, including any of the preceding embodiments,calculating the bone intensity value is performed at least in part byapplying a weighting function to the ratio of the first ultrasound datavalue and the first shadow intensity value, wherein the weightingfunction is parameterized by the at least one bone separation parameter.

In some embodiments, including any of the preceding embodiments, the atleast one bone separation parameter is calculated based, at least inpart, on the shadow intensity data.

In some embodiments, including any of the preceding embodiments, theultrasound data comprises a plurality of ultrasound data values eachcorresponding to a respective voxel in a plurality of voxels, andwherein generating the indication of tissue presence in the imagedregion comprises calculating, for a first of the plurality of voxels, atissue intensity value based at least in part on a first shadowintensity value corresponding to the first voxel.

In some embodiments, including any of the preceding embodiments,calculating the tissue intensity value is performed at least in part byevaluating a weighting function at least in part by using the firstshadow intensity value, wherein the weighting function is parameterizedby the at least one tissue separation parameter.

In some embodiments, including any of the preceding embodiments, theweighting function is a sigmoidal weighting function.

In some embodiments, including any of the preceding embodiments, the atleast one tissue separation parameter is calculated based, at least inpart, on the shadow intensity data.

In some embodiments, including any of the preceding embodiments,combining the indication of bone presence and the indication of tissuepresence is performed based, at least in part, on a desiredbone-to-tissue contrast and/or a desired contrast-to-noise ratio.

Some embodiments are directed to a method for visualizing ultrasounddata. The method comprises using at least one hardware processor toperform obtaining a two-dimensional (2D) ultrasound image of an imagedregion of a subject, the imaged region comprising bone; identifying across-section of a three-dimensional (3D) model of the bonecorresponding to the 2D ultrasound image at least in part by registeringthe 2D ultrasound image to a three-dimensional (3D) model; identifyingat least one location of at least one landmark feature of the bone inthe 2D ultrasound image based on results of the registration; andgenerating a visualization of the 2D ultrasound image and the identifiedcross-section of the 3D model of the bone, wherein the visualizationindicates the at least one location of the at least one landmarkfeature.

Some embodiments are directed to a system for visualizing ultrasounddata. The system comprises at least one computer hardware processorconfigured to perform obtaining a two-dimensional (2D) ultrasound imageof an imaged region of a subject, the imaged region comprising bone;identifying a cross-section of a three-dimensional (3D) model of thebone corresponding to the 2D ultrasound image at least in part byregistering the 2D ultrasound image to a three-dimensional (3D) model;identifying at least one location of at least one landmark feature ofthe bone in the 2D ultrasound image based on results of theregistration; and generating a visualization of the 2D ultrasound imageand the identified cross-section of the 3D model of the bone, whereinthe visualization indicates the at least one location of the at leastone landmark feature.

Some embodiments are directed to at least one non-transitorycomputer-readable storage medium storing processor executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor to performa method of visualizing ultrasound data. The method comprises obtaininga two-dimensional (2D) ultrasound image of an imaged region of asubject, the imaged region comprising bone identifying a cross-sectionof a three-dimensional (3D) model of the bone corresponding to the 2Dultrasound image at least in part by registering the 2D ultrasound imageto a three-dimensional (3D) model; identifying at least one location ofat least one landmark feature of the bone in the 2D ultrasound imagebased on results of the registration; and generating a visualization ofthe 2D ultrasound image and the identified cross-section of the 3D modelof the bone, wherein the visualization indicates the at least onelocation of the at least one landmark feature.

In some embodiments, generating the visualization comprises overlayingthe identified cross-section on the 2D ultrasound image.

In some embodiments, including any of the preceding embodiments, theoverlaying comprises performing an affine transformation of theidentified cross-section of the 3D model.

In some embodiments, including any of the preceding embodiments, theoverlaying comprises overlaying the identified cross-section on the 2Dultrasound image with a degree of transparency, the degree oftransparency determined using a measure of quality of fit between the 2Dultrasound image and the identified cross-section.

In some embodiments, including any of the preceding embodiments,generating the visualization further comprises generating thevisualization to include at least a portion of the 3D model of the boneand information identifying how the 2D ultrasound image corresponds tothe 3D model of the bone.

In some embodiments, including any of the preceding embodiments, theimaged region of the subject includes at least a portion of thesubject's spine and the 3D model of the bone comprises a 3D model of atleast the portion of a spine.

In some embodiments, including any of the preceding embodiments, the atleast one landmark feature of the bone comprises a spinous process of alumbar spine and/or an interlaminar space of a lumbar spine.

In some embodiments, including any of the preceding embodiments, theregistering is performed at least in part by using information aboutmotion of the subject during generation of the 2D ultrasound image.

Some embodiments, including any of the preceding embodiments, furthercomprise displaying the generated visualization.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects and embodiments of the technology will be described withreference to the following figures. It should be appreciated that thefigures are not necessarily drawn to scale. Items appearing in multiplefigures are indicated by the same reference number in all the figures inwhich they appear.

FIG. 1 is a block diagram of an exemplary apparatus that may include atleast one ultrasound transducer and at least one processor configured toperform model-based bone imaging, the output of which may be rendered tothe apparatus display, in accordance with some embodiments of thedisclosure provided herein.

FIG. 2 is a block diagram of an exemplary procedure by which model-basedbone imaging may be performed, in accordance with some embodiments ofthe disclosure provided herein.

FIG. 3 illustrates a visualization of a 2D ultrasound image of an imagedarea together with a 3D model of at least a portion of the imaged area,in accordance with some embodiments of the disclosure provided herein.

FIG. 4 illustrates a visualization of a 2D ultrasound image overlaid ona corresponding cross-section of a 3D model, in accordance with someembodiments of the disclosure provided herein.

FIG. 5 is a diagram illustrating the calculation of a bone filter, inaccordance with some embodiments of the disclosure provided herein.

FIG. 6 is a flowchart of an illustrative process of forming abone-enhanced image, in accordance with some embodiments of thedisclosure provided herein.

FIG. 7 illustrates the application of the imaging techniques describedherein to forming an image of a chicken bone, in accordance with someembodiments of the disclosure provided herein.

FIG. 8 is a flowchart of an illustrative process of generating anultrasound image, in accordance with some embodiments of the disclosureprovided herein.

FIG. 9 is a flowchart of an illustrative process of generating avisualization of a 2D ultrasound image and corresponding cross-sectionof a 3D bone model, in accordance with some embodiments of thedisclosure provided herein.

FIG. 10 is a block diagram of an illustrative computer system on whichembodiments described herein may be implemented.

DETAILED DESCRIPTION

The inventors have recognized that, when imaging an area of a subjectthat includes bone and tissue, identifying regions of bone and tissuepresence may help to generate improved ultrasound images of the imagedarea that may be easier to interpret. The regions of bone and tissuepresence may each be identified by taking into account ultrasound shadowcaused by bone presence in the imaged area. Accordingly, in someembodiments, an ultrasound image of a subject may be generated by: (1)obtaining ultrasound data generated based on ultrasound signals from animaged region of the subject; (2) generating shadow intensity datacorresponding to the ultrasound data; (3) generating indications of boneand tissue presence based on the generated shadow intensity data; and(4) combining the indications of bone and tissue presence. In this way,ultrasound images having a desired bone-to-tissue contrast and/or adesired contrast-to-noise ratio may be obtained, and such images may beeasier to interpret.

The inventors have also recognized that an ultrasound image comprisingbone may be easier to interpret if presented (e.g., to a user) withreference to an anatomical model of the bone being imaged. Accordingly,some embodiments relate to visualizing ultrasound data by generating avisualization of a two-dimensional (2D) ultrasound image that includes acorresponding portion of a three-dimensional (3D) bone model. Thecorresponding portion of the 3D model (e.g., a 2D cross-section) may beidentified at least in part by using a registration technique toregister the 2D ultrasound image to the 3D model. The registrationresults may be used to identify the location(s) of one or moreanatomical landmarks in the 2D ultrasound image and the generatedvisualization of the image may indicate one or more of the identifiedlocations.

Aspects of the technology described herein are explained in the contextof spinal anesthesia guidance, but it should be appreciated that thetechnology described herein is useful for and may be applied in othersettings. For example, the technology described herein may be used forother clinical applications where ultrasound is used to image bone suchas, but not limited to, guiding of orthopedic joint injections,performing lumbar punctures, or performing diagnosis of bone fractures.

In some embodiments, a method for performing ultrasound imaging isprovided. The method may comprise enhancing bone contrast by using thereciprocal of a shadow intensity value at every pixel location in anultrasound image, where the shadow intensity value may be defined as:

${S\left( {i,j} \right)} = {\sum\limits_{k = {i + \alpha}}^{M}{w_{k,i}{I\left( {k,j} \right)}}}$wherein S(i,j) is the shadow intensity output, I(i,j) is the envelopedetected ultrasound image data, w_(k,i) is a depth weighting, and a isan offset.

In some embodiments, the method comprises registering at least one 2Dultrasound image to a 3D model of a region comprising bone; andproducing a 2D and/or 3D visualization of the region comprising bonewherein the visualization is derived, at least in part, from theregistration of the at least one 2D ultrasound image to the 3D model ofthe spine.

The aspects and embodiments described above, as well as additionalaspects and embodiments, are described further below. These aspectsand/or embodiments may be used individually, all together, or in anycombination of two or more, as the technology described herein is notlimited in this respect.

FIG. 1 illustrates an example of an apparatus 100 that may be used forgenerating and/or displaying ultrasound images. As shown, apparatus 100comprises at least one processor circuit 104, at least one ultrasoundtransducer 106, at least one ultrasound signal conditioning circuit 112,at least one motion sensor 114, at least one memory circuit 116, anddisplay 118. The one or more ultrasound transducers 106 may beconfigured to generate ultrasonic energy 108 to be directed at a targettissue structure 110 within a subject being imaged (e.g., the ultrasoundtransducers 106 may be configured to insonify one or more regions ofinterest within the subject). Some of the ultrasonic energy 108 may bereflected by the target tissue structure 110, and at least some of thereflected ultrasonic energy may be received by the ultrasoundtransducers 106. In some embodiments, the at least one ultrasonictransducer 106 may form a portion of an ultrasonic transducer array,which may be placed in contact with a surface (e.g., skin) of a subjectbeing imaged.

In some embodiments, ultrasonic energy reflected by the subject beingimaged may be received by ultrasonic transducer(s) 106 and/or by one ormore other ultrasonic transducers, such as one or more ultrasonictransducers part of a linear transducer array. The ultrasonictransducer(s) that receive the reflected ultrasonic energy may begeometrically arranged in any suitable way (e.g., as an annular array, apiston array, a linear array, a two-dimensional array) or in any othersuitable way, as aspects of the disclosure provided herein are notlimited in this respect. As illustrated in FIG. 1, ultrasonictransducer(s) 106 may be coupled to the ultrasonic signal conditioningcircuit 112, which is shown as being coupled to circuits in apparatus100 via bus 120. The ultrasonic signal conditioning circuit 112 mayinclude various types of circuitry for use in connection with ultrasoundimaging such as beam-forming circuitry, for example. As other examples,the ultrasonic signal conditioning circuit may comprise circuitryconfigured to amplify, phase-shift, time-gate, filter, and/or otherwisecondition received ultrasonic information (e.g., echo information), suchas provided to the processor circuit 104.

In some embodiments, the receive path from each transducer element partof a transducer array, such as an array including the first ultrasonictransducer 106, may include one or more of a low noise amplifier, amain-stage amplifier, a band-pass filter, a low-pass filter, and ananalog-to-digital converter. In some embodiments, one or more signalconditioning steps may be performed digitally, for example by using theprocessor circuit 104.

In some embodiments, the apparatus 100 may be configured to obtainultrasonic echo information corresponding to one or more planesperpendicular to the surface of an array of ultrasound transducers(e.g., to provide “B-mode” imaging information). For example, theapparatus 100 may be configured to obtain information corresponding toone or more planes parallel to the surface of an array of ultrasoundtransducers (e.g., to provide a “C-mode” ultrasound image of loci in aplane parallel to the surface of the transducer array at a specifieddepth within the tissue of the subject). In an example where more thanone plane is collected, a three-dimensional set of ultrasonic echoinformation may be collected.

In some embodiments, the processor circuit 104 may be coupled to one ormore non-transitory computer-readable media, such as the memory circuit116, a disk, or one or more other memory technology or storage devices.In some embodiments, a combination of one or more of the firstultrasonic transducer 106, the signal conditioning circuit 112, theprocessor circuit 104, the memory circuit 116, a display 118, or a userinput device 102 may be included as a portion of an ultrasound imagingapparatus. The ultrasound imaging apparatus may include one or moreultrasound transducers 106 configured to obtain depth information viareflections of ultrasonic energy from an echogenic target tissuestructure 110, which may be a bone target.

In an example, the processor circuit 104 (or one or more other processorcircuits) may be communicatively coupled (e.g., via bus 120) to one ormore of a user input device 102 and the display 118. For example, theuser input device 102 may include one or more of a keypad, a keyboard(e.g., located near or on a portion of ultrasound scanning assembly, orincluded as a portion of a workstation configured to present ormanipulate ultrasound imaging information), a mouse, a touch-screencontrol, a rotary control (e.g., a knob or rotary encoder), a soft-keytouchscreen aligned with a portion of the display 118, and/or one ormore other controls of any suitable type.

In some embodiments, the processor circuit 104 may be configured toperform model registration-based imaging and presenting the constructedimage or images to the user via the display 118. For example, asimultaneous 2D/3D display may be presented to the user via the display118, as described in further examples below.

In some embodiments, ultrasonic energy reflected from target tissue 110may be obtained or sampled after signal conditioning through theultrasound signal conditional circuit 112 as the apparatus 100 is sweptor moved across a range of locations along the subject surface (e.g.,skin). A composite may be constructed such as using information aboutthe location of at least the transducer 106 of apparatus 100 (or theentire apparatus), such as provided by the motion sensor 114, andinformation about reflected ultrasonic energy obtained by the ultrasonictransducer 106. Motion sensor 114 may be any suitable type of sensorconfigured to obtain information about motion of the subject beingimaged (e.g., position information, velocity information, accelerationinformation, pose information, etc.). For example, the motion sensor 114may comprise one or more accelerometers configured to sense accelerationalong one or more axes. As another example, the motion sensor 114 maycomprise one or more optical sensors. The motion sensor 114 may beconfigured to use one or more other techniques to sense relative motionand/or absolute position of the apparatus 100, such as usingelectromagnetic, magnetic, optical, or acoustic techniques, or agyroscope, independently of the received ultrasound imaging information(e.g., without requiring motion tracking based on the position of imagedobjects determined according to received ultrasonic information).Information from the motion sensor 114 and ultrasonic energy obtained bythe ultrasonic transducer 104 may be sent to the processor circuit 104via bus 120. The processor circuit 104 may be configured to determinemotion or positional information of at least the transducer of apparatus100 using processes described in further examples below. The motion orpositional information may be used to carry out model registration-basedimaging.

Other techniques may include using one or more transducers that may bemechanically scanned, such as to provide imaging information similar tothe information provided by a two-dimensional array, but withoutrequiring the user to manually reposition the apparatus 100 during amedical procedure. The apparatus 100 may be small and portable, suchthat a user (e.g., a physician or nurse) may easily transport itthroughout healthcare facilities or it may be a traditional cart-basedultrasound apparatus.

In some embodiments, apparatus 100 may provide imaging usingnon-ionizing energy, as it may be safe, portable, low cost, and mayprovide an apparatus or technique to align a location or insertion angleof a probe to reach a desired target depth or anatomical location.Examples of the model registration-based process described below arefocused on spinal anesthesia clinical procedures whereby a healthcareprofessional inserts a probe in or around the spinal bone anatomy todeliver anesthetics. In this instance the model registration-basedprocess uses a 3D model of the spinal bone anatomy. However, theapparatus and methods described herein are not limited to being used forimaging of the spine and may be used to image any suitable bone orbones. In addition, apparatus 100 may be employed in clinical diagnosticor interventional procedures such as orthopedic joint injections, lumbarpunctures, bone fracture diagnosis, and/or guidance of orthopedicsurgery.

It should be appreciated that the apparatus 100 described with referenceto FIG. 1 is an illustrative and non-limiting example of an apparatusconfigured to perform ultrasound imaging in accordance with embodimentsof the disclosure provided herein. Many variations of apparatus 100 arepossible. For example, in some embodiments, an ultrasound imagingapparatus may comprise one or more transducers for generating ultrasonicenergy and circuitry to receive and process energy reflected by a targetbeing imaged to generate one or more ultrasound images of the subject,but may not comprise a display to display the images. Instead, in someembodiments, an ultrasound imaging apparatus may be configured togenerate one or more ultrasound images and may be coupled to one or moreexternal displays to present the generated ultrasound images to one ormore users.

FIG. 2 is a block diagram of an illustrative process 200 for ultrasoundimaging, in accordance with some embodiments of the disclosure providedherein. Process 200 may be performed by any suitable system or apparatussuch as a portable apparatus (e.g., apparatus 100 described withreference to FIG. 1) or a fixed apparatus.

One branch of process 200 begins at act 202, when ultrasound frame datais received. The ultrasound frame data may be ultrasound echo data(e.g., radio frequency or ‘RF’ signal data), which has been sent to aprocessor circuit 104 after conditioning with an ultrasound signalconditioning circuit 112. The ultrasound frame data received at act 202may be conditioned at acts 204-210 prior to being used to generate a 2Dimage. As illustrated in FIG. 2, the ultrasound frame data may bedemodulated into a complex baseband signal (IQ demodulation) and bandpass filtered at act 204. Subsequently, envelope detection may beperformed at act 206. Subsequently, range compression may be performedat act 208 and scan conversion may be performed at act 210. Rangecompression 208 may be performed using a logarithm mapping function orany other suitable function to increase the dynamic range of the image.Scan conversion 210 may be performed when ultrasound frame data is innon-rectilinear coordinates, such as polar coordinates. Some of theabove-discussed acts are described in more detail below.

In some embodiments, a bone filter may be applied to ultrasound framedata after the frame data has been demodulated, band pass filtered, andenvelope detection has been performed. This is shown by the arrow fromact 206 to act 216. The bone filter may operate on ultrasound frame dataafter envelope detection (real baseband signal) is performed at act 206.This remaining branch of the block diagram relates to the inventivemodel registration-based imaging approach. In some embodiments, a“fitting” or registration act may be performed between a 3D bone model224, such as a lumbar spine model, and the ultrasound 2D image orcompilation of ultrasound 2D images after extracting certain bonesurface point locations 220. Finally, in one embodiment, robust motiondetection 238 may support accurate fitting or registration.

In some embodiments, frame data 202 may be obtained from one or moreultrasound sensors (e.g., a linear array of ultrasound sensors, atwo-dimensional array of ultrasound sensors, one or more pistonultrasound transducers, etc.). The ultrasound sensor(s) may beconfigured to convert detected acoustic ultrasound energy into areceived electronic “echo trace” that is digitally sampled (e.g., byusing analog to digital converters), which is a component of theultrasound signal conditioning circuit 112. Various analog or digitalfiltering may be performed before the digitally sampled frame data istransferred to a microprocessor unit. The frame data may compriseA-lines obtained from different spatial locations along the scan plane.In the linear array for instance, this may be achieved by electronicallytranslating the transmit and/or receive apertures along the array. Inthe piston transducer, this may be achieved by mechanically sweeping thetransducer about an arc and collecting A-lines at different positionsalong the arc.

Bandpass filtering and IQ demodulation may be performed at act 204 usingone or more quadrature filters or in any other suitable way. Quadraturefilters may be two separate filters that are 90 degrees out of phasefrom one another but otherwise having the same bandwidth. The bandwidthand number of samples, or “taps”, for the set of filters may be chosenbased on the desired center frequency and roll-off. Filtering may beperformed by convolving each filter, an in phase (I) and quadrature (Q)filter, by each of the A-lines. The output may be twice the size of theoriginal frame data and may comprise I and Q components derived from theconvolution of the I and Q quadrature filters. Other methods to IQdemodulate a radio-frequency signal include multiplication by twoversions of a sinusoidal carrier signal 90 degrees out of phase witheach other (I and Q), followed by low-pass filtering to remove one ofthe modulation images, leaving only the I and Q baseband signalcomponents.

In some embodiments, performing envelope detection (e.g., at act 206)may comprise computing the magnitude of each I and Q sample combination,treated as a complex number, (I real, Q imaginary). For example ifI(i,j) and Q(i,j) are the sample values from the ith row and jth columnof the I or Q components, respectively, then the envelope-detectedoutput is computed as the magnitude of the two values:√{square root over (I(i,j)² +Q(i,j)²)}.

At act 208, range compression may be performed on the envelope detectedsignal data. Range compression may comprise computing a logarithm (e.g.,base 10) of the ultrasound data or square root or some other similarmapping function that may increase the dynamic range of the 2D displayimage pixel data sent to the apparatus display 118 via a bus 120. Themapping function may be adjusted depending on the imaging parameters214, such as gain or contrast. For instance, the mapping functionM(P(i,j)) that maps pixel P(i,j) to a range compressed output value mayinclude an offset that has the effect of changing gain, for exampleshifting P(i,j) values higher or lower: M(P(i,j)+t). For t>0, gain isincreased thereby providing for an overall higher amplitude image.

At act 210, scan conversion 210 may be performed to convertrange-compressed data from a non-Cartesian coordinate system (e.g., apolar coordinate system) to a Cartesian coordinate system. Inembodiments where the ultrasound data is obtained (e.g., sampled) in theCartesian coordinate system, as the case may be with linear array-basedimaging, then scan conversion 210 may not be needed.

At act 212, the ranged compressed (and optionally scan-converted) datamay be used to generate an image for display to a user. For example, ifprocess 200 were performed by using apparatus 100, act 212 may beperformed at least in part by transferring data from the apparatusprocessor circuit 104 to the apparatus user display 118 via bus 120.

In some embodiments, imaging parameters 214 may be set by the user byway of the apparatus user input device 102 and may include, for example,zoom, depth, gain, image contrast, or bone-to-tissue contrast. Though,in other embodiments, one or more of the imaging parameters 214 may beset automatically. In some embodiments, the image parameters may affectthe output of the bone filter 216, scan conversion 218, or simultaneous2D/3D image display. For example, in some embodiments, the bone filter216 may be computed only over the depth range set by the imagingparameters and therefore reduce the amount of computational resourcesused and/or the time needed to perform the computations.

In ultrasound imaging, bone surfaces may be characterized as brightlyreflecting interfaces followed by an (ultrasound) “shadow.” The termultrasound shadow refers to the substantial absence of a reflectedultrasound signal from one or more imaged areas because of the presenceof one or more objects (e.g., a bone) that reflect(s) at least some(e.g., all) of the ultrasound energy passing through the object(s). Ashadow generally occurs when imaging a bone surface because theultrasound waves do not pass through the bone surface and are insteadmostly reflected at the bone surface.

Accordingly, in some embodiments, a priori knowledge of bone surfacereflections may be used to enhance bone surfaces in an ultrasound imagewhile at least partially attenuating other soft tissue regions in theultrasound image. Such enhancement and/or attenuation may be performedat least in part by using a bone-filtering step (e.g., step 216described herein). An image obtained by using a bone-filtering step maypossess an enhanced delineation of bone structures as compared to theunfiltered image. The bone filter computes, in some embodiments, a“shadow intensity” value for each of one or more (e.g., every) pixel inthe envelope detected frame data (e.g., as computed at act 206 ofillustrative process 200). The shadow intensity may be computed as aweighted sum of image intensity values at all image depths greater thanthe current pixel value with a range offset. Therefore, bone surfacelocations, which exhibit substantial shadowing, may exhibit a low shadowintensity value while regions of soft tissue will exhibit a relativelyhigher shadow intensity value. In some embodiments, the bone-filteredimage may be obtained by multiplying each of one or more pixels in theenvelope detected frame data by the reciprocal of each pixel'srespective shadow intensity value. One or more additional functions maybe used to combine the shadow intensity information with the imageintensity values (e.g., envelope-detected frame data) with the goal ofproducing a desired image output that possesses enhanced bone-to-tissuecontrast or contrast-to-noise ratio (CNR) when compared with theoriginal, unfiltered frame data.

Scan conversion 218 may be performed on the output of the bonefiltering, for example, in the same manner as described with respect toscan conversion 210 performed on the range compressed 208 image data.

In one embodiment, the output of the bone filtering performed at act 216may be scan converted, at act 218, and displayed to a user (e.g., via adisplay such as display 118). In another embodiment, a modelregistration-based approach may be configured to yield a display withboth the 2D bone filter output along with information indicative of theregistration output including the position and scale of the model targettissue after registration to the image data. An initial step to registerthe scan converted bone filtered image to a 3D bone model 224 may beperformed based at least in part on bone surface point locationsextracted, at act 220, from the scan converted bone filtered outputobtained at act 218. Bone surface points may be extracted automatically.For example, in some embodiments, bone surface points may be extractedby setting an image intensity threshold such that values in the scanconverted bone filtered output 218 above a threshold are automaticallyidentified as possible bone surface locations to be used to perform theregistration of the possible bone surface locations to the 3D model atact 222. In another example, an algorithm may first locate groups ofconsecutive pixels along A-lines with intensities greater than athreshold value. This threshold value may be adaptively set as amultiple of the mean value from the bone filter output 216. Within eachgrouping of pixels, a single point may be extracted to more efficientlyrepresent that segment of bone. This single point can, for example,correspond to the point location with the maximum bone filter outputvalue or maximum shadow intensity. The extracted point locations andtheir bone filter output values or shadow intensities may then beaccumulated into a vector for registration with the 3D bone model 224.

In some embodiments, the act 222 of registration to a 3D model maycomprise performing point set registration, which may compriseidentifying a translation and/or scaling of one of two sets of pointdata that minimizes a cost function or “similarity metric.” An examplecost function involves Euclidean distance and image intensity of the“best match”. In embodiments where a point set registration method isapplied, a first set of points may be extracted from both bone filteredframe data (e.g., the extracted bone surface point locations obtained atact 220 of process 200) and a second set of points may be extracted fromthe 3D bone model 224. In the 3D bone model 224, the point set may beeasily accessed if the 3D bone model is formatted in a computer aideddesign (CAD) file type such as an .stl file. The vertices from the .stllist may be used as the point set. Frame-to-frame displacementinformation and previous registration outputs, e.g. model position,scaling, and rotation, may be used to inform the 3D model registration222. For example, if zero displacement between frames is detected, thenthe previous registration solution is highly likely compared toregistration solutions with greatly varied translation, scaling, orrotation. Therefore, the translation and scaling solutions correspondingto the previous registration solution may be assigned a higherweighting.

It should be appreciated that other methods besides point setregistration may be used to perform the registration at act 222 ofprocess 200. As one illustrative example, template matching may be used,whereby registration is performed directly between the 3D model and thescan converted bone filtered image 218. In such an embodiment, the actof extracting bone surface point locations may be omitted. Differentmetrics may be used to find the optimal registration output. Forexample, the maximum image intensity summed along the intersections ofthe 3D model and the image may be found for different translations (thex, y, and z dimensional shifts of the 2D image along the 3D volume) andscaling (the multiplication of the pixel size dimension relative to the3D model inter-element spacing where an element is a point or surfacecomprising the 3D volume). The correct registration would correspond toa translation and scaling combination that results in the maximum summedintensity.

Another illustrative non-limiting example of a registration techniquethat may be performed at act 222 of process 200 is a coarse-to-fineregistration technique. A coarse-to-fine registration technique may takeas inputs the extracted bone surface locations from one or more scanconverted bone filtered image frames and the 3D bone model 224. Thepoint set obtained from the 3D bone model may be translated along aspatial grid relative to the set of points extracted from the bonefiltered image frame(s) 220. The 3D bone model may be allowed to scaleor rotate about a similar parameter grid with a grid spacing and gridextent. At each positional, scaling, and rotation combination along thegrid, a quantity from a cost function may be calculated. The initialgrid extent and interval spacing may be larger in the coarseregistration phase compared with fine registration. An example costfunction used in a preferred embodiment is the following:

$\begin{matrix}{{cost} = {\sum\limits_{i = 1}^{N}{\min\limits_{j \in {\lbrack{1,M}\rbrack}}\left( \frac{{dist}\left( {{{template}(i)},{{bone}(j)}} \right)}{{intensity}\left( {{bone}(j)} \right)} \right)}}} & (1)\end{matrix}$where N is the total number of points in the 3D bone model, M is thetotal number of points extracted from the bone filtered image frame,‘dist’ is a distance calculation between the model point ‘i’ and thebone filtered image point ‘j’, and ‘intensity’ is the bone filtered orshadow intensity value of the pixel containing the correspondingextracted bone surface point ‘j’. The cost value represents a minimumvalue by associating, for each template point, a bone surface point thatminimizes a distance to bone surface intensity ratio. The coarseregistration positional, scaling, and rotational solution is chosen asthe combination along the parameter grid that produces the lowestregistration score using the cost function, such as the cost function ofEquation (1). Finally, the fine registration may be performed. Thepurpose of the fine registration is to produce a more accurate measureof the registration output. The accuracy of the coarse registration maybe limited due to forced positional, scaling, and rotational gridsampling. The fine registration starts with the coarse registrationsolution and allows for much more fine sampling but over a smallerparameter grid extent, which is centered about the coarse registrationresolution. Alternatively, a fine registration process may includeindividually registering control points in the 3D bone model to pointsin the extracted bone point set from the bone filtered image frame 220using the same cost function in Equation (1). Control points aretypically chosen as landmark points along the model geometry, such asspinous process, lamina, or vertebral body.

The inventors have recognized the clinical benefits of automaticallylocating certain bony landmarks for the ultrasound apparatus user. Inthis way, the user is not required to interpret the 2D B-mode or scanconverted bone-filtered image. Instead the model registration-basedapproach may identify and locate certain anatomy for the user. Theregistration output 222, such as a point set registration, is a set oftranslation, scaling, or rotational parameters. The location of bonylandmarks, such as spinal bone landmarks, may be read from the 3D modelafter applying the prescribed translation, scaling, and rotation. In theapplication of imaging a lumbar spine, for example, the landmarks mayinclude the spinous process and interlaminar space. These landmark depthlocations may be useful to present to the user as they may help toinform the depth of a needle insertion attempt, such as in a neuraxialanesthesia procedure. The 2D/3D image display 230 may presentinformation indicative of the location of these landmarks to the user onthe apparatus user display 118 as will be described in further exemplaryembodiments below.

Additionally, the translation, scaling, and rotational parameters,termed the 3D model transformation parameters 226, may be applied to the3D bone model representation 224 for the purpose of displaying thetransformed 3D model to the user display screen 118. These modeltransformation parameters 226 are also useful for a 2D image displaywhere a portion of the 3D model is shown as an overlay to a 2D image.The overlay may represent the intersection of the 2D image plane and the3D registered bone model. For example, FIGS. 2-3 illustrate a 2Dcross-sectional overlay in a 2D image region of the display.

In the application of ultrasound-guided spinal anesthesia, a 3D lumbarspine bone model is suggested as the 3D bone model 224. The model may bea computer aided design (CAD) model. It may be of various file formatsincluding .stl and .dicom. The model may be obtained from computedtomography (CT) or from manual graphics rendering. A set of models maybe used if desirable where the model yielding the greatest cost functionminimization, such as lowest Euclidean distance, is used for thedisplay. Other bone structures may be used as the 3D bone model, such asfemur, knee, or hip. A 3D point-set bone model representation may becaptured with the use of as few as two separate 2D cross-sections. In apreferred embodiment applied to 3D spinal bone models, transversecross-section “model template” vectors may be used. Each model templatemay represent key landmark anatomy such as the spinous process orvertebral body landmark anatomy. Templates may be defined prior toimaging with the goal of having a size and shape representative of thehuman population. The 3D point-set spine representation may be morefinely sampled, with many model templates, with the trade-off ofincreased computational time.

Output of the model registration-based process may be displayed to auser at act 230. In some embodiments, the output may comprise a 2D imageof a subject being imaged and/or a 3D image of a corresponding 3D bonemodel.

As one illustrative non-limiting example, a user may be presented with a2D ultrasound image derived from a range compressed and scan converted2D image frame 212 or a bone filtered and scan converted image frame218; a 3D representation of the bone model after transformation based onthe registration 3D model transformation parameters 226; landmarklocations 228 automatically identified and highlighted in any suitableway (e.g., by using color, transparency, shading, overlaid indicators,etc.); indicators in the 3D display that show the location of thecurrent 2D ultrasound scan plane relative to the 3D bone modelregistration output; and indicators to indicate a “goodness-of-fit” oruncertainty relative to the registration process. For example, theuncertainty value may be based on the minimized cost function outputassociated with the registration solution, such as in Equation (1). Thisinformation displayed to the user is superior as compared to aconventional 3D rendering, which does not utilize automatic landmarklocalization or registration, because it may provide a more intuitivedisplay with measurement of interest automated. For example, the model3D image may be noiseless and exhibit perfect sensitivity to bonedetection. The landmarks do not necessarily require manual selection andtranslation measurements, as they may be automatically located fordisplay on the apparatus user display 118. Overall, the approach allowsthe user to visualize both 2D and 3D images and the 2D location withrespect to the 3D volume.

The inventors have also recognized that motion or positional sensing maybe used to improve the robustness and accuracy of the modelregistration-based process. In an exemplary embodiment, image data maybe obtained, for example, from a motion sensor such as camera 232. Forexample, a series of image frames from a CMOS cell-phone class camerasensor may be captured at successive time intervals. Camera motiondetection 236 may be performed using a motion detection algorithm thatexamines two or more camera image frames and uses differences betweenthe frames to estimate relative motion between the camera and what thecamera is imaging. Any of numerous types of motion estimation algorithmsmay be used including, but not limited to, 2D normalizedcross-correlation and 2D sum-absolute-difference. The output of cameramotion detection may form an estimate of relative camera motion, in 2 ormore dimensions, in addition to a measure of estimate quality, forexample estimated standard deviation of one or more measurementcomponents.

An ultrasound motion detection 234 algorithm examines two or moreultrasound image frames and uses differences between the frames toestimate relative motion between the ultrasound transducer and thetarget. Relative motion may be estimated using any of numerous types ofmotion estimation algorithms may be used including, but not limited to,2D normalized cross-correlation and 2D sum-absolute-difference. Formotion out of the scan plane, the statistical decorrelation propertiesof ultrasound speckle may be used, with optional training data, to forma motion estimate. Still other techniques may be used. The output ofultrasound motion detection is an estimate of relative ultrasoundtransducer/tissue motion in up to 3 dimensions, along with a measure ofestimate quality, for example estimated standard deviation of one ormore measurement components.

The motion estimates from camera motion detection 236 and ultrasoundmotion detection 234 may be combined to form robust motion detection238. The two input motion estimates may be combined using estimatequality values, (e.g. standard deviation or similar statistical qualitymeasure). One form of estimate combination is to assume that both inputestimates are independent, normally distributed variables, and to sumvalues from both sources, weighted by the inverse of the individualstandard deviations, forming a maximum likelihood combined estimate.However, other methods of combining two individual estimates could beused, as aspects of the disclosure provided herein are not limited inthis respect. In each case, the combined motion estimate should have, onaverage, less error than each individual estimate.

Further specifications and exemplary embodiments related to the bonefilter 216 will now be recited. In some embodiments, the bone filterfirst computes shadow intensity values for one or more (e.g., every)locations in the frame data. A shadow intensity may be calculated as aweighted sum of all image intensity values at the same scan line but atall depths greater than the current depth plus an offset, α:

$\begin{matrix}{{S\left( {i,j} \right)} = {\sum\limits_{k = {i + \alpha}}^{M}{w_{k,i}{I\left( {k,j} \right)}}}} & (2)\end{matrix}$where S(i,j) is the shadow intensity output, I(i,j) is the envelopedetected ultrasound image data, and w_(k,i) is a depth weighting, whichvaries with k and i. The indices i range from 1 through the M number ofdepth samples in I. The index j ranges from 1 through the N number ofscan lines. The weighting values w_(k,i) are typically constant with kand chosen as a function only of i such that the output S(i,j)corresponds to the average envelope detected values in column j from i+αthrough M. However, in other embodiments the weightings may be variablesuch as to be more or less sensitive to pixel locations further orcloser to the current pixel location k, j. In some embodiments, theoffset a is determined as the thickness in the range, or depth,dimension of a bone surface in the envelope detected ultrasound data. Inthis way, if pixel depth location i corresponds to a bone surface point,then the shadow intensity output sums only over regions of signaldropout (i.e. shadow) rather than incorporating signal from bone. Thatis to say, if pixel depth location i were located at the leading, mostshallow, edge of a bone surface, then pixel locations i through i+(α−1)are comprised of signal from the bone surface while i+α through Mlocations are comprised of shadow only. The exact value of a may bedetermined by experimental observation or derived from the axialresolution of the imaging system.

The output of the bone filter may then be calculated as the pointwisedivision of the envelope detected ultrasound image with the shadowintensity values with an additional factor, T, which is chosen as asmall number in order to avoid division by 0.B(i,j)=I(i,j)/(S(i,j)+i)  (3)

It should be appreciated that the bone filter output may be formed usinga function other than a pointwise-division as described with respect toEquation 3. For example, a sigmoidal function may be used. An exemplaryembodiment is illustrated in FIG. 5. The column of envelope detectedA-line data 508 is illustrated for each entry i where the values mimicthe typical pattern of tissue (medium intensity) 502, then typicalvalues for bone surface (high intensity) 504, then typical values foracoustic shadow (intensity dropout) 506. The value of j, in this set ofembodiments, is constant due to only one A-line being illustrated. Thecolumn of corresponding shadow intensity values 510 shows the shadowintensity values that result from the example envelope detected A-linevalues 508 using filter parameters 514 and Equation 2. As illustrated bythe filter parameters 514, an a value of 3 is used as it corresponds tothe thickness of the bone region 504. The value of τ=0.01, is a smallnumber relative to I(i,j) values and avoids division by 0. Values of thebone filter output 512 are rounded to the nearest whole number. Valuesof the depth weighting function w_(k,i) are illustrated in theparameters 514 and only vary with i and are constant with k. The valuesare chosen such that shadow intensity outputs are an average across thei+α through M summation. The bone filter output 512 in this exemplaryembodiment is the element-wise product of the envelope detected A-linesvalues 508 with the reciprocal of shadow intensity values 510, againrounded to the nearest whole number. As illustrated in FIG. 5, theoutput of the bone filter 512 exhibits an improved contrast between boneregions 504 and tissue regions 502 (20:1 versus 20:5). Entries with theasterisk correspond to locations where a shadow intensity value cannotbe computed because i+α>M. In an exemplary embodiment, these entrieswould be filled with zeros or some other number.

Another exemplary embodiment of a procedure in which bone or bonedeformation along a bone surface is detected, enhanced, or identifiedfrom the received ultrasound energy is illustrated in the block diagramFIG. 6. This exemplary procedure is based on computation of a shadowintensity value and a bone filter output described, such as described inEquation 3, as a basis for separating bone and tissue components. Withbone and tissue regions segmented, the final “bone enhanced” image 620may be reconstructed with arbitrary contrast or contrast-to-noise orsome other similar image quality metric that may be user defined. Inthis embodiment the “bone enhanced” image 620 may be displayed to thedevice display 118 in addition, or as a substitution, to the bone filteroutput 216 described by Equation 3. The exemplary embodiment of a bonefilter described in FIG. 6 takes as an input envelope-detected framedata, I(i,j) 602, such as generated from the FIG. 2 embodiment at theoutput of the envelope detection 206 step. A speckle reduction 606preprocessing step may be performed to improve performance of theultimate signal separation 610 into a tissue component 624 and bonecomponent 622. The speckle reduction steps may comprise, in oneembodiment, a combined approach of wavelet transform thresholding andbilateral filtering. In one embodiment, the discrete wavelet transformmay be computed using Daubechies wavelets (or any other suitablewavelets) and thresholding may be performed on coefficients in thelateral high, combination high, and axial high frequency sub-images.After zeroing wavelet coefficients below a threshold in each sub-image,the inverse discrete wavelet transform may be applied, and finally,bilateral filtering. In another embodiment, bilateral filtering alonemay be used to reduce image speckle.

The reciprocal of the shadow intensity 604 may then be computed from theenvelope-detected and speckle reduced frame data 606, O(i,j), using thesame expression in Equation 2 with the exception that the input data isspeckle reduced, O(i,j), rather than being the originalenvelope-detected frame data, I(i,j). The bone filter output is thencomputed by multiplication 608 of the envelope-detected and specklereduced frame data 606 by the reciprocal of the shadow intensity 604according to Equation 3, with the exception that the input data isspeckle reduced, O(i,j), rather than being the originalenvelope-detected frame data, I(i,j). Signal separation 610 may then beperformed. In one embodiment, the extraction of the bone component 622may be achieved using a sigmoidal weighting function with the bonefilter output from the multiplication 608 step according to Equation 3as the basis for separation as follows:Y _(B)(i,j)=1/(1+e ^(−γ) ^(B) ^((B(i,Dj)−τ) ^(B) ⁾)  (4)where γ_(B) is a parameter of the sigmoidal function that changes theroll-off, τ_(B) is the bone separation threshold parameter, and B(i,j)is the bone filter output according to Equation 3, corresponding to themultiplication 608 of the envelope-detected and speckle reduced framedata 606 by the reciprocal of the shadow intensity 604. Y_(B)(i,j) ofEquation 4 represents the bone component 622. The sigmoidal functionparameters, γ_(B) and τ_(B) may be set as fixed values or may beadaptive to the image data, such as by setting the values to a valueproportional to the mean value of the shadow intensity reciprocal 604 oroutput of the multiplication 608 with the shadow intensity reciprocal604 and the envelope-detected speckle reduced frame data 606.

Extraction of the tissue component 612 may be achieved in a similarmanner using a sigmoidal weighting function with the shadow intensityreciprocal 604 as the basis for separation. A representative tissueextraction equation is as follows:

$\begin{matrix}{{Y_{T}\left( {i,j} \right)} = {1/\left( {1 + e^{- {\gamma_{T}{({\frac{1}{S{({i,j})}} - \tau_{T}})}}}} \right)}} & (5)\end{matrix}$where γ_(T) is again a parameter of the sigmoidal function that changesthe roll-off, τ_(T) is the tissue separation threshold, and 1/S(i,j) isthe reciprocal of the shadow intensity 604. The Y_(T) parameterrepresents the tissue component 624. The sigmoidal function parameters,γ_(T) and τ_(T) may be set as fixed values or may be adaptive to theimage data, such as by setting the values to a value proportional to themean value of the original envelope-detected frame data 602.

After bone and tissue component separation, tissue amplitude mapping 612and bone amplitude mapping 614 is performed prior to the final summationof the components 618 to form the bone enhanced image 620. The boneamplitude mapping function may take a number of forms but, in someembodiments, may be equal to the bone component Y_(B)(i,j) 622 fromEquation 4. Depending on parameters used in Equation 4, this strategymay result in image regions with positive detection of bone generallyexhibiting saturation at the highest image intensity level—in thisexemplary embodiment, 1.0.

With the assumption that the bone amplitude mapping function 614achieves a mean bone image intensity of 1.0, the purpose of the tissueamplitude mapping function 612 is to set the tissue mean and standarddeviation such that a user-defined parameter input 616 is achieved inthe final bone enhanced image result 620. These user-defined parameters616 may include, for example, bone-to-tissue contrast and CNR. Contrast,C, and contrast-to-noise, CNR, may be defined as follows:

$\begin{matrix}{C = {20{\log_{10}\left( {\mu_{bone}/\mu_{tissue}} \right)}}} & (6) \\{{CNR} = {20\log_{10}\;\left( \frac{{\mu_{bone} - \mu_{tissue}}}{\sigma_{tissue}} \right)}} & (7)\end{matrix}$where μ_(bone), μ_(tissue), σ_(tissue) are the mean and standarddeviation of the bone and tissue regions in the image, respectively.Therefore, the goal of the tissue amplitude mapping function is to setthe tissue component mean and standard deviation such that Equations. 6and 7 provide the desired C and CNR of the final bone enhancement image620. These target metrics may be achieved using the followingconsecutively performed steps:

$\begin{matrix}{{{Step}\mspace{14mu} 1\text{:}\mspace{14mu}{M_{T}\left( {i,j} \right)}} = {{{I\left( {i,j} \right)}{Y_{T}\left( {i,j} \right)}} - {\overset{\hat{}}{\mu}}_{tissue}}} & (7) \\{{{Step}\mspace{14mu} 2\text{:}\mspace{14mu}{M_{T}\left( {i,j} \right)}} = {\frac{\sigma_{desired}}{{\hat{\sigma}}_{tissue}}\mspace{14mu}{M_{T}\left( {i,j} \right)}}} & (8) \\{{{Step}\mspace{14mu} 3\text{:}\mspace{14mu}{M_{T}\left( {i,j} \right)}} = {{M_{T}\left( {i,j} \right)} + \mu_{desired}}} & (9)\end{matrix}$where I(i,j) is the original envelope-detected imaging data prior tospeckle reduction 602, M_(T)(i,j) is the tissue amplitude mapping 612output, {circumflex over (μ)}_(tissue) and {circumflex over(σ)}_(tissue) are the estimated mean and standard deviation of thetissue component 624 of the original image, Y_(T)(i,j), and μ_(desired)and σ_(desired) are the desired mean and standard deviation of the finalreconstructed image in regions representing tissue. Values forμ_(desired) and σ_(desired) may be chosen to provide the desiredcontrast and CNR.

The final step in the bone enhancement process 600 is to reconstruct theimage by summing 618 the tissue amplitude mapping output with the boneamplitude mapping output to form the bone enhanced image 620.

FIG. 7 illustrates an exemplary result from a bone enhancement processsuch as those described in FIGS. 2, 5, and 6. As may be appreciated fromthe images shown, the bone enhancement process described herein allowsfor detection of deformations in bone that are less than the originalresolution of the ultrasound system. For example, illustrated in FIG. 7is a photograph of a chicken bone 704 where a small 2.38 mm hole wascreated that is smaller than the resolution expected from an ultrasoundsystem used to capture ultrasound echo data from the same bone surface.In FIG. 7 a standard ultrasound B-mode 702 of the chicken bone 704 isdemonstrated. It may be easily seen from the B-mode image 704 that theholes 712 are not resolvable in the standard B-mode image 710. That isto say that the B-mode image 704 in regions corresponding to the holes712 do not exhibit a clearly distinct image intensity from that of thesurrounding bone surface 710. However, using the bone enhancementtechnology described herein (e.g. FIG. 6), the holes 712 becomes clearlyresolvable. That is to say that the image intensity from the boneenhancement image 620 is clearly distinct from that of the surroundingbone surface 710. It is clear in the bone enhancement image 620 thatthere is a gap in the bone surface 710 corresponding to a hole 712. Thisis an unexpected and clinically useful result of the aforementioned boneimaging inventive concepts. Certain prior art has taught methods of boneenhancement that operate after envelope-detected data has been processedto create image data, e.g. B-mode images 702. This image data was thenlow-pass filtered and then edge detected before quantifying the shadowstrength. However, as FIG. 7 illustrates, the B-mode image 702 dataformed from the received ultrasound echo data does not enable detectionof small deformations in the bone that are less than the resolution ofthe ultrasound system. In contrast, the current inventors havediscovered bone enhancement processes using shadow filter values derivedfrom the envelope-detected form of the received ultrasound echo data orafter applying certain speckle reduction processes to theenvelope-detected data. These steps enable the bone enhancement image620 result of FIG. 7 whereby small deformations become easily visible.Detecting features smaller than the resolution of the ultrasound systemis useful in clinical applications where localization of small gaps orfeatures in the bone surfaces is desirable. These applications mayinclude, for example, such fracture detection or guidance of injectionsin or around bone, such as in epidurals or joint injections.

A variety of methods may be employed to create display the simultaneous2D/3D image display 230. Exemplary embodiments are illustrated in FIGS.3-4. In some embodiments, the 2D/3D display 230 may contain both a 2Dimage region 304 and 3D image region 308. A line and or other depthindicator 306 may be overlaid on the 2D image region 304 to indicate thelocation of a spine landmark, such as a spinous process 302 orinterlaminar space 318. In 3D image regions 308, a dashed line or otherindicator may be overlaid on the 3D rendering 314 to indicate thelocation of the current 2D image cross-section 316 relative to the 3Drendering 314. A circle or other indicator 312 overlaid on the 3D imagerendering 314 may be displayed to indicate uncertainty in the locationof the current 2D image cross-section 316 relative to the 3D rendering314. In some embodiments, a semi-transparent cross-section 404 of the 3Dbone model 224 may be overlaid to the 2D image regions 304. Thesemi-transparent cross-section 404 derived from the 3D model 224 mayhave its position and dimensions correspond to the output of thecorresponding 3D model registration 222. The amount of transparency maybe scaled in proportion to the certainty associated with thecorresponding 3D model registration 222. For example, the transparencylevel may be proportional to a minimized cost function value from theregistration process, such as the cost value computed using Equation 1.A lower cost function value would indicate a registration with higherconfidence and the cross-section display from the model, in thisexample, would be less. Furthermore, identification of certain bonylandmarks in the 2D image display region 304 may be conveyed using colorand/or in any other suitable way (e.g., shading, overlaid indicators,text, etc.). In an exemplary embodiment, the semi-transparentcross-section 404 overlay, the 3D rendering 314, and landmark positionindicator 306 may be presented in a blue color if a spinous processlandmark is located in the 2D image display 304 during the 3D modelregistration process 222. Conversely, if the interlaminar space islocated in the 2D image display 304 during the 3D model registrationprocess 222, then the semi-transparent cross-section 306 and 3Drendering 314 may be colored orange. In this embodiment, a color maplegend 402 in the 3D display region 308 may be included to aid the userin discerning the color-coding for different spinal landmarks detectedin the image.

In some embodiments, the display may contain only the 2D image portionsof FIGS. 3-4 where the registration process serves to automaticallylocate landmark depths and/or to provide a 2D cross-section overlay 404.Likewise, only the 3D image portions of FIGS. 3-4 could be shown on thedisplay, in some embodiments. In some embodiments, the 3D image portionsmay exhibit motion from frame-to-frame, which may be determined from themotion estimates and/or registration output. Alternatively, thecross-section position indicator lines 310 could move relative to the 3Dspine, in some embodiments.

Certain inventive aspects relate to the use of the bone enhancementfilter to operate on ultrasound imaging data. The following are featuresthat may be used individually or in combination (in combination witheach other and/or in combination with other inventive features describedelsewhere throughout) in association with certain embodiments. In someembodiments, an offset a may be used that accounts for the bonethickness such that locations in S with lower shadow values correspondto locations in the envelope-detected frame data I with high intensityvalues of the bone surfaces. In some embodiments, the methods describedherein may be applied to the envelope detected ultrasound data, asopposed to a fully processed (envelope detected and log compressed)ultrasound image that has been blurred and then summed with an edgedetection version of the blurred image. According to certainembodiments, the reciprocal of the shadow intensity is multiplied by theenveloped detected image intensity. Accordingly, in some embodiments,the shadow intensity values are not self-normalized and subsequentlymultiplied by the sum of the blurred image with the edge detected outputof the blurred image.

Some embodiments of the disclosure provided herein are described belowwith reference to FIGS. 8 and 9. FIG. 8 is a flowchart of anillustrative process 800 of generating an ultrasound image, inaccordance with some embodiments of the disclosure provided herein.Process 800 may be executed by any suitable device and, for example, maybe executed by a device comprising one or more ultrasonic transducers(e.g., apparatus 100 described above with reference to FIG. 1), by adevice that does not include any ultrasound transducers, by a computersystem such as computer system 1000 described below with reference toFIG. 10), multiple computing devices, and/or by any other suitabledevice or devices.

Process 800 begins at act 802, where ultrasound data is obtained by thedevice executing process 800. In embodiments where the device executingprocess 800 comprises one or more ultrasound transducers, the ultrasounddata may be obtained from the ultrasound transducer(s) that are part ofthe device. In other embodiments, regardless of whether the deviceexecuting process 800 comprises one or more ultrasound transducers, theultrasound data may be obtained from another device with which thedevice executing process 800 is configured to communicate.

Ultrasound data obtained at act 802 may be any suitable type ofultrasound data and, for example, may be ultrasound frame data. In someembodiments, ultrasound data obtained at act 802 may be the ultrasoundframe data described with reference to act 202 of FIG. 2. In someembodiments, ultrasound data may comprise a plurality of ultrasound datavalues each corresponding to a respective voxel in a set of voxels. Thevalue of a voxel may correspond to a value of the subject being imagedat a location in three-dimensional space. As one non-limiting example,the value of a voxel may be a value indicative of an amount ofultrasound energy reflected from the subject at a location inthree-dimensional space.

In some embodiments, the obtained ultrasound data may be processed(either before being obtained or after being obtained as part of process800) using one or more suitable signal processing techniques. Forexample, in some embodiments, ultrasound data obtained at act 802 mayhave been demodulated, band pass filtered, and envelope detection mayhave been applied to the ultrasound data. In some embodiments, one ormore of demodulation, band pass filtering, and envelope detection may beapplied to the ultrasound data after it is received at act 802.

Next, process 800 proceeds to act 804 where shadow intensity data,corresponding to the ultrasound data obtained at act 802, is calculated.In some embodiments, where the ultrasound data comprises ultrasound datavalues each corresponding to a voxel in a set of voxels, calculating theshadow intensity data may comprise calculating a shadow intensity valuefor one or more voxels in the set of voxels. A shadow intensity valuefor a voxel may be calculated at least in part by calculating a weightedsum of ultrasound data values corresponding to voxels at least athreshold number of voxels away from the first voxel. As one example, ashadow intensity value for a voxel (i,j) may be calculated according toEquation (2) described above, where the constant α is the thresholdnumber of voxels. The threshold number of voxels may be any suitablenumber (e.g., 0, 1, 2, 3, 5, 10, etc.) of voxels and may be set manuallyor automatically. In some embodiments, the threshold number of voxelsmay be set such that the voxels whose values are used to calculate theshadow intensity value do not correspond to locations in or on thesurface of a bone. In some embodiments, the threshold number of voxelsmay be greater than or equal to an axial resolution of the imagingsystem used to generate the ultrasound data. It should be appreciatedthat shadow intensity data may be obtained in any other suitable way, asaspects of the disclosure provided herein are not limited in thisrespect.

After shadow intensity data is calculated at act 804, process 800proceeds to act 806, where an indication of bone presence in an imagedregion of a subject is generated. The indication of bone presence mayprovide an indication, for each of one or more voxels in the imagedregion of a subject, whether bone is present at the location in thesubject to which the voxel corresponds. Calculating an indication ofbone presence at a particular voxel may comprise calculating a boneintensity value, which may indicate a likelihood of bone presence suchthat higher (or, in another embodiment, lower) values indicate anincreased likelihood of bone presence and lower (or, in anotherembodiment, higher) values indicate a decreased likelihood of bonepresence. In some embodiments, a bone intensity value for a voxel may becalculated based at least in part on a ratio of an ultrasound data valuecorresponding to the voxel (obtained at act 802) and a shadow intensityvalue corresponding to the voxel (obtained at act 804). The boneintensity value may be obtained at least in part by applying a function(e.g., a sigmoidal weighting function) to the ratio of the ultrasounddata value corresponding to the voxel and the shadow intensity valuecorresponding to the voxel. The function may depend on one or more boneseparation parameters, each of which may be set as fixed values or maybe calculated based at least in part on ultrasound data (obtained at act802) and/or shadow intensity data (obtained at act 804).

As one non-limiting example, the indication of bone presence may becalculated according to Equation (4) described above. In particular,Equation (4) may be used to calculate one or more bone intensity valuesusing a function parameterized by two bone separation parameters γ_(B)and τ_(B). One or both of these parameters may be calculated based, atleast in part, on the shadow intensity data, as described above withreference to Equation (4). It should be appreciated, however, thatEquation (4) is an illustrative non-limiting example of how to calculatebone intensity values and that bone intensity values may be calculatedin any other suitable way.

Next, process 800 proceeds to act 808, where an indication of tissuepresence in an imaged region of a subject is generated. The indicationof tissue presence may provide an indication, for each of one or morevoxels in the imaged region of a subject, whether tissue is present atthe location in the subject to which the voxel corresponds. Calculatingan indication of tissue presence at a particular voxel may comprisecalculating a tissue intensity value, which may indicate a likelihood oftissue presence such that higher (or, in another embodiment, lower)values indicate an increased likelihood of tissue presence and lower(or, in another embodiment, higher) values indicate a decreasedlikelihood of tissue presence. In some embodiments, a tissue intensityvalue for a voxel may be calculated based at least in part the shadowintensity value corresponding to the voxel. The tissue intensity valuemay be calculated by evaluating a function (e.g., a sigmoidal weightingfunction) at least in part by using the shadow intensity valuecorresponding to the voxel. The function may depend on one or moretissue separation parameters, each of which may be set as fixed valuesor may be calculated based at least in part on ultrasound data (obtainedat act 802) and/or shadow intensity data (obtained at act 804).

As one non-limiting example, the indication of tissue presence may becalculated according to Equation (5) described above. In particular,Equation (5) may be used to calculate one or more tissue intensityvalues using a function parameterized by two tissue separationparameters γ_(T) and τ_(T). One or both of these parameters may becalculated based, at least in part, on the ultrasound data obtained atact 802 (e.g., based on envelope-detected frame data, as described abovewith reference to Equation (5)). It should be appreciated that Equation(5) is an illustrative non-limiting example of how to calculate tissueintensity values and that tissue intensity values may be calculated inany other suitable way.

In some embodiments, indications of bone and tissue presence may becalculated using one or more bone separation parameters different fromone or more tissue separation parameters. As one example, the parametersγ_(B) and γ_(T) in Equations (4) and (5) may have different values.Additionally, or alternatively, the parameters τ_(B) and τ_(T) inEquations (4) and (5) may have different values. As may be appreciatedfrom the foregoing, in some embodiments, the indications of bone andtissue presence may be calculated independently from one another ratherthan being derived from one another. That is, in some embodiments, theindication of tissue presence is not derived from the indication of bonepresence (e.g., by calculating a tissue intensity value for a voxel as 1minus bone intensity value for the voxel), but is computed directly fromthe shadow intensity data.

Next, process 800 proceeds to act 810, where an ultrasound image isgenerated, at least in part, by using the indications of bone presenceand tissue presence obtained at act 806 and 808, respectively. This maybe done in any suitable way. In some embodiments, the indications ofbone and tissue presence may be combined to form an ultrasound imagehaving a desired bone-to-tissue contrast and/or a desiredcontrast-to-noise ratio. This may be done as described above withreference to Equations (6)-(9) or in any other suitable way. After act810 is executed, process 800 completes.

FIG. 9 is a flowchart of illustrative process 900 of generating avisualization of a 2D ultrasound image and a corresponding cross-sectionof a 3D bone model, in accordance with some embodiments of thedisclosure provided herein. Process 900 may be executed by any suitabledevice and, for example, may be executed by a device comprising one ormore ultrasonic transducers (e.g., apparatus 100 described above withreference to FIG. 1), by a device that does not include any ultrasoundtransducers, by a computer system such as computer system 1000 describedbelow with reference to FIG. 10), multiple computing devices, and/or byany other suitable device or devices.

Process 900 begins at act 902, where a two-dimensional (2D) ultrasoundimage of an imaged region of a subject is obtained. The imaged regionmay comprise bone. For example, the imaged region may comprise at leasta portion of the spine (e.g., lumbar spine) of a subject being imagedand/or any other suitable bone of a subject, as aspects of thedisclosure provided herein are not limited to imaging of any particularbone(s) of the subject and may be applied to imaging any bone(s) of thesubject. The two-dimensional ultrasound image may be obtained using anyof the techniques described herein (e.g., process 800) or in any othersuitable way.

Next, process 900 proceeds to act 904 where a portion of athree-dimensional (3D) model of the bone corresponding to the 2Dultrasound image is identified. In some embodiments, the 3D bone modelcomprises two or more 2D cross sections and act 904 comprisesidentifying a 2D cross section of the 3D model corresponding to theultrasound image obtained at act 902. As described above, in someembodiments, a 2D cross section of a 3D bone model may comprise one ormore “model template” vectors each of which may represent one or moreanatomical landmarks (e.g., one or more vertebral landmarks, one or morespinous processes, one or more interlaminar spaces, etc.).

In some embodiments, the portion of a 3D model of the bone correspondingto the 2D ultrasound image may be identified by using a registrationtechnique. Any of the above-described registration techniques or anyother suitable registration technique(s) may be used to identify theportion of the 3D model of the bone corresponding to the 2D ultrasoundimage, as aspects of the disclosure provided herein are not limited inthis respect. In some embodiments, the registration may be performed atleast in part by using information about motion of the subject duringgeneration of the 2D ultrasound image. In this way, any motion by thesubject during imaging may be taken into account when identifying across-section of the 3D model of the bone that corresponds to the imageof the subject obtained while the subject was moving.

Next, process 900 proceeds to act 906, where the location(s) of one ormore anatomical landmarks of the subject are identified in the 2Dultrasound image based on results of the registration. It should beappreciated that, unlike some conventional approaches to performingregistration by first identifying anatomical landmarks and performingregistration based on the identified anatomical landmarks, theanatomical landmarks are not used to perform the registration in process900, in some embodiments. Rather, anatomical landmarks may be identifiedbased on results of the registration process at act 906 of process 900,and this may be done in any suitable way. As one illustrative example,the 3D model of the bone may indicate one or more anatomical landmarksand the results of the registration may be used to identifycorresponding anatomical landmarks in the 2D ultrasound image.

Next, process 900 proceeds to act 908, where a visualization of the 2Dultrasound image and identified cross-section of the 3D model isgenerated. The visualization may indicate the location of one or moreanatomical landmarks identified at act 906. For example, in theapplication of imaging a lumbar spine, the visualization may indicatethe location of the spinous process and/or an interlaminar space.

In some embodiments, generating the visualization may compriseoverlaying the identified 2D cross section on the 2D ultrasound image(see e.g., FIG. 4). Performing the overlaying may comprise performing anaffine transformation of the identified 2D cross section so that thecross-section and the ultrasound image line up when displayed. In someembodiments, generating the visualization may comprise generating thevisualization to include at least a portion of the 3D model of the boneand information identifying how the 2D ultrasound image correspond tothe 3D model of the bone, as illustrated in FIG. 4, for example.

In some embodiments, the identified 2D cross section is overlaid on the2D ultrasound image with a degree of transparency that is determinedbased, at least in part, on results of the registration. The degree oftransparency may be determined using a measure of quality of fit betweenthe 2D ultrasound image and the identified cross section. Any suitablemeasure of fit may be used (e.g., a measure of uncertainty associatedwith the registration, Equation (1), a goodness-of-fit metric, Euclideandistance, etc.), as aspects of the disclosure provided herein are notlimited in this respect. In some embodiments, the degree of transparencymay be inversely proportional to the goodness of fit. For example, thebetter the fit between the 2D ultrasound image and the identified 2Dcross section of the 3D bone model, the less transparency may be used tooverlay the identified 2D cross section on the ultrasound image.Similarly, the worse the fit between the 2D ultrasound image and theidentified 2D cross section of the 3D bone model, the more transparencymay be used to overlay the identified 2D cross-section on the ultrasoundimage. In this way, transparency may be used to reduce impact of poorregistration results on the user.

Next, process 900 proceeds to act 910, where the visualization generatedat act 908 is displayed. The visualization may be displayed using thedevice executing process 900 (e.g., device 100 described with referenceto FIG. 1) or any other suitable device(s) (e.g., one or more displays),as aspects of the disclosure provided herein are not limited in thisrespect. After act 910 is performed, process 900 completes.

Having thus described several aspects and embodiments of the technologyof this application, it is to be appreciated that various alterations,modifications, and improvements will readily occur to those of ordinaryskill in the art. Such alterations, modifications, and improvements areintended to be within the spirit and scope of the technology describedin the application. For example, those of ordinary skill in the art willreadily envision a variety of other means and/or structures forperforming the function and/or obtaining the results and/or one or moreof the advantages described herein, and each of such variations and/ormodifications is deemed to be within the scope of the embodimentsdescribed herein. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific embodiments described herein. It is, therefore, to beunderstood that the foregoing embodiments are presented by way ofexample only and that, within the scope of the appended claims andequivalents thereto, inventive embodiments may be practiced otherwisethan as specifically described. In addition, any combination of two ormore features, systems, articles, materials, kits, and/or methodsdescribed herein, if such features, systems, articles, materials, kits,and/or methods are not mutually inconsistent, is included within thescope of the present disclosure.

The above-described embodiments may be implemented in any of numerousways. One or more aspects and embodiments of the present applicationinvolving the performance of processes or methods may utilize programinstructions executable by a device (e.g., a computer, a processor, orother device) to perform, or control performance of, the processes ormethods. In this respect, various inventive concepts may be embodied asa computer readable storage medium (or multiple computer readablestorage media) (e.g., a computer memory, one or more floppy discs,compact discs, optical discs, magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement one or more of the variousembodiments described above. The computer readable medium or media maybe transportable, such that the program or programs stored thereon maybe loaded onto one or more different computers or other processors toimplement various ones of the aspects described above. In someembodiments, computer readable media may be non-transitory media.

An illustrative implementation of a computer system 1000 that may beused in connection with any of the embodiments of the disclosureprovided herein is shown in FIG. 10. The computer system 1000 mayinclude one or more processors 1010 and one or more articles ofmanufacture that comprise non-transitory computer-readable storage media(e.g., memory 1020 and one or more non-volatile storage media 1030). Theprocessor 1010 may control writing data to and reading data from thememory 1020 and the non-volatile storage device 1030 in any suitablemanner, as the aspects of the disclosure provided herein are not limitedin this respect. To perform any of the functionality described herein,the processor 1010 may execute one or more processor-executableinstructions stored in one or more non-transitory computer-readablestorage media (e.g., the memory 1020), which may serve as non-transitorycomputer-readable storage media storing processor-executableinstructions for execution by the processor 1010.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that may be employed to program a computer or otherprocessor to implement various aspects as described above. Additionally,it should be appreciated that according to one aspect, one or morecomputer programs that when executed perform methods of the presentapplication need not reside on a single computer or processor, but maybe distributed in a modular fashion among a number of differentcomputers or processors to implement various aspects of the presentapplication.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically, the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconvey relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

When implemented in software, the software code may be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers.

Further, it should be appreciated that a computer may be embodied in anyof a number of forms, such as a rack-mounted computer, a desktopcomputer, a laptop computer, or a tablet computer, as non-limitingexamples. Additionally, a computer may be embedded in a device notgenerally regarded as a computer but with suitable processingcapabilities, including a Personal Digital Assistant (PDA), a smartphone or any other suitable portable or fixed electronic device.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that may be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that may be used for a userinterface include keyboards, and pointing devices, such as mice, touchpads, and digitizing tablets. As another example, a computer may receiveinput information through speech recognition or in other audibleformats.

Such computers may be interconnected by one or more networks in anysuitable form, including a local area network or a wide area network,such as an enterprise network, and intelligent network (IN) or theInternet. Such networks may be based on any suitable technology and mayoperate according to any suitable protocol and may include wirelessnetworks or wired networks.

Also, as described, some aspects may be embodied as one or more methods.The acts performed as part of the method may be ordered in any suitableway. Accordingly, embodiments may be constructed in which acts areperformed in an order different than illustrated, which may includeperforming some acts simultaneously, even though shown as sequentialacts in illustrative embodiments.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Elements other than those specificallyidentified by the “and/or” clause may optionally be present, whetherrelated or unrelated to those elements specifically identified. Thus, asa non-limiting example, a reference to “A and/or B”, when used inconjunction with open-ended language such as “comprising” may refer, inone embodiment, to A only (optionally including elements other than B);in another embodiment, to B only (optionally including elements otherthan A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) mayrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

As used herein, the term “between” is to be inclusive unless indicatedotherwise. For example, “between A and B” includes A and B unlessindicated otherwise.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively.

What is claimed is:
 1. A processor-based method for visualizingultrasound data, the method comprising: obtaining ultrasound data,including image intensity values, that represents a two-dimensional (2D)ultrasound image of an imaged region of a subject, the imaged regioncomprising bone, the ultrasound data including pixel locations havingrespective pixel depths; applying a bone filter to the ultrasound datato determine possible bone surface locations; providing athree-dimensional (3D) simulated model of a human bone having a size andshape representative of a human population, wherein the 3D simulatedmodel is defined prior to obtaining the 2D ultrasound image of suchsubject; identifying model template cross-sections of the (3D) simulatedmodel of the human bone corresponding to the 2D ultrasound imagecomprising registering the possible bone surface locations in the 2Dultrasound image to the 3D simulated model, identifying at least onelocation of at least one landmark feature of the bone in the 2Dultrasound image based on results of the registration; and generating avisualization that includes: (a) a visualization of the 2D ultrasoundimage that comprises the bone and (b) a visualization of one of theidentified cross-sections of the 3D simulated model of the human bonethat were defined prior to obtaining the 2D ultrasound image andidentified at least in part by registering the 2D ultrasound image tothe 3D simulated model, wherein the visualization indicates the at leastone location of the at least one landmark feature.
 2. Theprocessor-based method of claim 1, wherein in the visualization thatincludes: (a) the visualization of the 2D ultrasound image thatcomprises the bone and (b) the visualization of one of the identifiedcross-sections of the 3D simulated model of the human bone that wasdefined prior to obtaining the 2D ultrasound image and identified atleast in part by registering the 2D ultrasound image to the simulated 3Dmodel, the visualization of the 2D ultrasound image that comprises thebone does not overlap the visualization of the one of the identifiedcross-sections of the 3D simulated model of the human bone that weredefined prior to obtaining the 2D ultrasound image and identified atleast in part by registering the 2D ultrasound image to the 3D simulatedmodel.
 3. The processor-based method of claim 1, wherein in thevisualization that includes: (a) the visualization of the 2D ultrasoundimage that comprises the bone and (b) the visualization of one of theidentified cross-sections of the 3D simulated model of the human bonethat was defined prior to obtaining the 2D ultrasound image andidentified at least in part by registering the 2D ultrasound image tothe 3D simulated model, the visualization of the 2D ultrasound imagethat comprises the bone is spaced apart from the visualization of theone of the identified cross-sections of the 3D simulated model of thehuman bone that were defined prior to obtaining the 2D ultrasound imageand identified at least in part by registering the 2D ultrasound imageto the 3D simulated model.
 4. The processor-based method of claim 1,wherein: the possible bone surface locations comprise a first set ofpoints extracted from the bone-filtered ultrasound data, and registeringthe possible bone surface locations in the 2D ultrasound image to the 3Dsimulated model comprises comparing the first set of points with asecond set of points extracted from the 3D simulated model of the humanbone.
 5. The processor-based method of claim 4, wherein comparing thefirst set of points with the second set of points comprises identifyinga translation and/or scaling of the first or second set of points thatminimizes a cost function.
 6. The processor-based method of claim 1,wherein registering the possible bone surface locations in the 2Dultrasound image to the 3D simulated model comprises determining amaximum image intensity summed along intersections of the 3D simulatedmodel and the 2D ultrasound image.
 7. The processor-based method ofclaim 6, wherein determining the maximum image intensity comprisesvarying a translation and a scaling of the 2D ultrasound image relativeto the 3D simulated model.
 8. The processor-based method of claim 1,wherein registering the possible bone surface locations in the 2Dultrasound image to the 3D simulated model comprises using acoarse-to-fine registration technique.
 9. At least one non-transitorycomputer readable storage medium storing processor-executableinstructions that, when executed by at least one processor, result in amethod comprising: obtaining ultrasound data, including image intensityvalues, that represent a two-dimensional (2D) ultrasound image of animaged region of a subject, the imaged region comprising bone, theultrasound data including pixel locations having respective pixeldepths; applying a bone filter to the ultrasound data to determinepossible bone surface locations; identifying model templatecross-sections of a three-dimensional (3D) simulated model of the bonecorresponding to the 2D ultrasound image at least in part by registeringthe possible bone surface locations in the 2D ultrasound image to the 3Dsimulated model, wherein the model template cross-sections are definedprior to obtaining such 2D ultrasound image of such subject, the modeltemplate cross-sections having a size and shape representative of apopulation of potential subjects; identifying at least one location ofat least one landmark feature of the bone in the 2D ultrasound imagebased on results of the registration; and generating a visualizationthat includes: (a) a visualization of the 2D ultrasound image thatcomprises the bone and (b) a visualization of one of the identifiedcross-sections of the 3D simulated model of the bone that was definedprior to obtaining the 2D ultrasound image and identified at least inpart by registering the 2D ultrasound image to 3D simulated model,wherein the visualization indicates the at least one location of the atleast one landmark feature.
 10. The at least one non-transitory computerreadable storage medium of claim 9, wherein in the visualization thatincludes: (a) the visualization of the 2D ultrasound image thatcomprises the bone and (b) the visualization of one of the identifiedcross-sections of the 3D simulated model of the bone that was definedprior to obtaining the 2D ultrasound image and identified at least inpart by registering the 2D ultrasound image to the 3D simulated model,the visualization of the 2D ultrasound image that comprises the bonedoes not overlap the visualization of the one of the identifiedcross-sections of the 3D simulated model of the bone that were definedprior to obtaining the 2D ultrasound image and identified at least inpart by registering the 2D ultrasound image to the 3D simulated model.11. The at least one non-transitory computer readable storage medium ofclaim 9, wherein in the visualization that includes: (a) thevisualization of the 2D ultrasound image that comprises the bone and (b)the visualization of one of the identified cross-sections of the 3Dsimulated model of the bone that was defined prior to obtaining the 2Dultrasound image and identified at least in part by registering the 2Dultrasound image to the 3D simulated model, the visualization of the 2Dultrasound image that comprises the bone is spaced apart from thevisualization of the one of the identified cross-sections of the 3Dsimulated model of the bone that were defined prior to obtaining the 2Dultrasound image and identified at least in part by registering the 2Dultrasound image to the 3D simulated model.
 12. A system for processingultrasound data, the system comprising: at least one computer hardwareprocessor; at least one ultrasound imaging unit coupled to saidprocessor; said processor including circuitry that obtains ultrasounddata, including image intensity values, that represent a two-dimensional(2D) ultrasound image of an imaged region of a subject, the imagedregion comprising bone, the ultrasound data including pixel locationshaving respective pixel depths; said processor including circuitry thatapplies a bone filter to the ultrasound data to determine possible bonesurface locations; said processor including circuitry that identifiesmodel template cross-sections of a three-dimensional (3D) simulatedmodel of the bone corresponding to the 2D ultrasound image at least inpart by registering the possible bone surface locations in the 2Dultrasound image to the 3D simulated model, wherein the model templatecross-sections are defined prior to obtaining such 2D ultrasound imageof such subject, the model template cross-sections having a size andshape representative of a population of potential subjects; saidprocessor including circuitry that identifies at least one location ofat least one landmark feature of the bone in the 2D ultrasound imagebased on results of the registration; and said processor includingcircuitry that generates a visualization that includes: (a) avisualization of the 2D ultrasound image that comprises the bone and (b)a visualization of one of the identified cross-sections of the 3Dsimulated model of the bone that were defined prior to obtaining the 2Dultrasound image and identified at least in part by registering the 2Dultrasound image to the 3D simulated model, wherein the visualizationindicates the at least one location of the at least one landmarkfeature.
 13. The system of claim 12, wherein in the visualization thatincludes: (a) the visualization of the 2D ultrasound image thatcomprises the bone and (b) the visualization of one of the identifiedcross-sections of the 3D simulated model of the bone that was definedprior to obtaining the 2D ultrasound image and identified at least inpart by registering the 2D ultrasound image to the 3D simulated model,the visualization of the 2D ultrasound image that comprises the bonedoes not overlap the visualization of the one of the identifiedcross-sections of the 3D simulated model of the bone that were definedprior to obtaining the 2D ultrasound image and identified at least inpart by registering the 2D ultrasound image to the 3D simulated model.14. The system of claim 12, wherein in the visualization that includes:(a) the visualization of the 2D ultrasound image that comprises the boneand (b) the visualization of one of the identified cross-sections of the3D simulated model of the bone that was defined prior to obtaining the2D ultrasound image and identified at least in part by registering the2D ultrasound image to the simulated model, the visualization of the 2Dultrasound image that comprises the bone is spaced apart from thevisualization of the one of the identified cross-sections of the 3Dsimulated model of the bone that were defined prior to obtaining the 2Dultrasound image and identified at least in part by registering the 2Dultrasound image to the 3D simulated model.